Here are preliminary results of the bibliometric mapping of the 2022 Luxembourg research evaluation. Its purpose is:
The method for the research-field-mapping can be reviewed here:
The seed articles deemed representative for the active areas of research in the institution, and include authors affiliated with the institution. They can be selected in three ways:
The present analysis is based on the following seed articles:
| AU | PY | TI | JI |
|---|---|---|---|
| GONDER S;FERNANDEZ BOTANA I… | 2020 | METHOD FOR THE ANALYSIS OF THE TUMOR MICROENVIRONMENT BY MASS CYTOMETRY: APPLICATION TO CHRONIC L… | FRONT. IMMUNOL. |
| DITTMAR G;WINKLHOFER KF | 2020 | LINEAR UBIQUITIN CHAINS: CELLULAR FUNCTIONS AND STRATEGIES FOR DETECTION AND QUANTIFICATION | FRONT. CHEM. |
| SCHLICKER L;BOERS HM;DUDEK … | 2019 | POSTPRANDIALMETABOLIC EFFECTS OF FIBERMIXES REVEALED BY IN VIVO STABLE ISOTOPE LABELING IN HUMANS | METABOLITES |
| DUFRESNE J;BOWDEN P;THAVARA… | 2018 | THE PLASMA PEPTIDOME 03 CHEMICAL SCIENCES 0301 ANALYTICAL CHEMISTRY | CLIN. PROTEOMICS |
| HIPP G;VAILLANT M;DIEDERICH… | 2018 | THE LUXEMBOURG PARKINSON’S STUDY: A COMPREHENSIVE APPROACH FOR STRATIFICATION AND EARLY DIAGNOSIS | FRONT. AGING NEUROSCI. |
| BAHLAWANE C;SCHMITZ M;LETEL… | 2017 | INSIGHTS INTO LIGAND STIMULATION EFFECTS ON GASTRO-INTESTINAL STROMAL TUMORS SIGNALLING | CELL. SIGNAL. |
Here, we report the results of a LDA topic-modelling (basically, clustering on words) on all title+abstract texts.
Note: While this static vies is helpful, I recommend using the interactive LDAVis version to be found under https://daniel-hain.github.io/biblio_lux_2022/output/topic_modelling/LDAviz_lih_tmoh.rds/index.html#topic=1&lambda=0.60&term=. For functionality and usage, see technical description in the next tab.
Topic modeling is a type of statistical modeling for discovering the abstract “topics” that occur in a collection of documents. Latent Dirichlet Allocation (LDA, Blei et al., 2003) is an example of topic model and is used to classify text in a document to a particular topic.
LDA is a generative probabilistic model that assumes each topic is a mixture over an underlying set of words, and each document is a mixture of over a set of topic probabilities. It builds a topic per document model and words per topic model, modeled as Dirichlet distributions.
LDAvis is a web-based interactive visualisation of topics estimated using LDA (Sievert & Shirley, 2014). It provides a global view of the topics (and how they differ from each other), while at the same time allowing for a deep inspection of the terms most highly associated with each individual topic. The package extracts information from a fitted LDA topic model to inform an interactive web-based visualization. The visualisation has two basic pieces.
The left panel visualise the topics as circles in the two-dimensional plane whose centres are determined by computing the Jensen–Shannon divergence between topics, and then by using multidimensional scaling to project the inter-topic distances onto two dimensions. Each topic’s overall prevalence is encoded using the areas of the circles.
The right panel depicts a horizontal bar chart whose bars represent the individual terms that are the most useful for interpreting the currently selected topic on the left. A pair of overlaid bars represent both the corpus-wide frequency of a given term as well as the topic-specific frequency of the term.
The \(\lambda\) slider allows to rank the terms according to term relevance. By default, the terms of a topic are ranked in decreasing order according their topic-specific probability ( \(\lambda\) = 1 ). Moving the slider allows to adjust the rank of terms based on much discriminatory (or “relevant”) are for the specific topic. The suggested optimal value of \(\lambda\) is 0.6.
Note: This analysis refers the co-citation analysis,
where the cited references and not the original publications are the
unit of analysis. See tab Technical descriptionfor
additional explanations
In order to partition networks into components or clusters, we deploy a community detection technique based on the Lovain Algorithm (Blondel et al., 2008). The Lovain Algorithm is a heuristic method that attempts to optimize the modularity of communities within a network by maximizing within- and minimizing between-community connectivity. We identify the following communities = knowledge bases.
| name | dgr_int | dgr |
|---|---|---|
| Knowledge Base 1: KB 1: unlabeled (n = 2065, density =3.11) | ||
| KOMANDER D. RAPE M. THE UBIQUITIN CODE (2012) | 7134 | 8794 |
| SWATEK K.N. KOMANDER D. UBIQUITIN MODIFICATIONS (2016) | 3971 | 4552 |
| YAU R. RAPE M. THE INCREASING COMPLEXITY OF THE UBIQUITIN CODE (2016) | 3682 | 3974 |
| HERSHKO A. CIECHANOVER A. THE UBIQUITIN SYSTEM (1998) | 3557 | 4494 |
| MEYER H.J. RAPE M. ENHANCED PROTEIN DEGRADATION BY BRANCHED UBIQUITIN CHAINS (2014) | 2449 | 2517 |
| HUSNJAK K. DIKIC I. UBIQUITIN-BINDING PROTEINS: DECODERS OF UBIQUITIN-MEDIATED CELLULAR FUNCTIONS (2012) | 2269 | 2641 |
| MEVISSEN T.E.T. KOMANDER D. MECHANISMS OF DEUBIQUITINASE SPECIFICITY AND REGULATION (2017) | 2154 | 2487 |
| YE Y. RAPE M. BUILDING UBIQUITIN CHAINS: E2 ENZYMES AT WORK (2009) | 1305 | 1347 |
| DESHAIES R.J. JOAZEIRO C.A. RING DOMAIN E3 UBIQUITIN LIGASES (2009) | 1275 | 1410 |
| WENZEL D.M. LISSOUNOV A. BRZOVIC P.S. KLEVIT R.E. UBCH7 REACTIVITY PROFILE REVEALS PARKIN AND HHARI TO BE RING/HECT HYBRIDS (2011) | 1236 | 1584 |
| Knowledge Base 2: KB 2: unlabeled (n = 1308, density =2.52) | ||
| HUGHES A.J. DANIEL S.E. KILFORD L. LEES A.J. ACCURACY OF CLINICAL DIAGNOSIS OF IDIOPATHIC PARKINSON’S DISEASE: A CLINICO-PATHOLOGICAL STUDY OF 100 … | 2178 | 2178 |
| GILMAN S. WENNING G.K. LOW P.A. SECOND CONSENSUS STATEMENT ON THE DIAGNOSIS OF MULTIPLE SYSTEM ATROPHY (2008) | 1151 | 1151 |
| POSTUMA R.B. BERG D. STERN M. MDS CLINICAL DIAGNOSTIC CRITERIA FOR PARKINSON’S DISEASE (2015) | 963 | 963 |
| LITVAN I. AGID Y. CALNE D. CLINICAL RESEARCH CRITERIA FOR THE DIAGNOSIS OF PROGRESSIVE SUPRANUCLEAR PALSY (STEELE-RICHARDSON-OLSZEWSKI SYNDROME) | 930 | 930 |
| HOEHN M.M. YAHR M.D. PARKINSONISM: ONSET PROGRESSION AND MORTALITY (1967) | 891 | 891 |
| TOMLINSON C.L. STOWE R. PATEL S. RICK C. GRAY R. CLARKE C.E. SYSTEMATIC REVIEW OF LEVODOPA DOSE EQUIVALENCY REPORTING IN PARKINSON’S DISEASE (2010) | 777 | 777 |
| BERG D. POSTUMA R.B. ADLER C.H. MDS RESEARCH CRITERIA FOR PRODROMAL PARKINSON’S DISEASE (2015) | 656 | 656 |
| GOLBE L.I. OHMAN-STRICKLAND P.A. A CLINICAL RATING SCALE FOR PROGRESSIVE SUPRANUCLEAR PALSY (2007) | 642 | 642 |
| EMRE M. AARSLAND D. BROWN R. CLINICAL DIAGNOSTIC CRITERIA FOR DEMENTIA ASSOCIATED WITH PARKINSON’S DISEASE (2007) | 539 | 539 |
| HELY M.A. REID W.G. ADENA M.A. HALLIDAY G.M. MORRIS J.G. THE SYDNEY MULTICENTER STUDY OF PARKINSON’S DISEASE: THE INEVITABILITY OF DEMENTIA AT 20 Y… | 435 | 435 |
| Knowledge Base 3: KB 3: unlabeled (n = 921, density =6.27) | ||
| BRUGGNER R.V. BODENMILLER B. DILL D.L. TIBSHIRANI R.J. NOLAN G.P. AUTOMATED IDENTIFICATION OF STRATIFYING SIGNATURES IN CELLULAR SUBPOPULATIONS (2014) | 1491 | 1491 |
| VAN DER MAATEN L. HINTON G. VISUALIZING DATA USING T-SNE (2008) | 1155 | 1158 |
| WEBER L.M. ROBINSON M.D. COMPARISON OF CLUSTERING METHODS FOR HIGH-DIMENSIONAL SINGLE-CELL FLOW AND MASS CYTOMETRY DATA (2016) | 1093 | 1093 |
| SPITZER M.H. NOLAN G.P. MASS CYTOMETRY: SINGLE CELLS MANY FEATURES (2016) | 1026 | 1026 |
| LEVINE J.H. DATA-DRIVEN PHENOTYPIC DISSECTION OF AML REVEALS PROGENITOR-LIKE CELLS THAT CORRELATE WITH PROGNOSIS (2015) | 948 | 948 |
| SAMUSIK N. GOOD Z. SPITZER M.H. DAVIS K.L. NOLAN G.P. AUTOMATED MAPPING OF PHENOTYPE SPACE WITH SINGLE-CELL DATA (2016) | 888 | 888 |
| BENDALL S.C. NOLAN G.P. ROEDERER M. CHATTOPADHYAY P.K. A DEEP PROFILER’S GUIDE TO CYTOMETRY (2012) | 762 | 762 |
| BANDURA D.R. BARANOV V.I. ORNATSKY O.I. ANTONOV A. KINACH R. LOU X. PAVLOV S. TANNER S.D. MASS CYTOMETRY: TECHNIQUE FOR REAL TIME SINGLE CELL MULTI… | 748 | 748 |
| SHEKHAR K. BRODIN P. DAVIS M.M. CHAKRABORTY A.K. AUTOMATIC CLASSIFICATION OF CELLULAR EXPRESSION BY NONLINEAR STOCHASTIC EMBEDDING (ACCENSE) | 698 | 698 |
| KOTECHA N. KRUTZIK P.O. IRISH J.M. WEB-BASED ANALYSIS AND PUBLICATION OF FLOW CYTOMETRY EXPERIMENTS (2010) | 679 | 679 |
| Knowledge Base 4: KB 4: unlabeled (n = 728, density =12.89) | ||
| MICHEAU O. TSCHOPP J. INDUCTION OF TNF RECEPTOR I-MEDIATED APOPTOSIS VIA TWO SEQUENTIAL SIGNALING COMPLEXES (2003) | 1353 | 2007 |
| HAAS T.L. EMMERICH C.H. GERLACH B. SCHMUKLE A.C. CORDIER S.M. RIESER E. FELTHAM R. WENGER T. RECRUITMENT OF THE LINEAR UBIQUITIN CHAIN ASSEMBLY COM… | 1251 | 1384 |
| KIRISAKO T. KAMEI K. MURATA S. KATO M. FUKUMOTO H. KANIE M. SANO S. IWAI K. A UBIQUITIN LIGASE COMPLEX ASSEMBLES LINEAR POLYUBIQUITIN CHAINS (2006) | 1212 | 1738 |
| DRABER P. KUPKA S. REICHERT M. DRABEROVA H. LAFONT E. DE MIGUEL D. SPILGIES L. HARTWIG T. LUBAC-RECRUITED CYLD AND A20 REGULATE GENE ACTIVATION AND… | 1055 | 1130 |
| GERLACH B. CORDIER S.M. SCHMUKLE A.C. EMMERICH C.H. RIESER E. HAAS T.L. WEBB A.I. WONG W.W. LINEAR UBIQUITINATION PREVENTS INFLAMMATION AND REGULAT… | 990 | 1090 |
| BERTRAND M.J. MILUTINOVIC S. DICKSON K.M. HO W.C. BOUDREAULT A. DURKIN J. GILLARD J.W. BARKER P.A. CIAP1 AND CIAP2 FACILITATE CANCER CELL SURVIVAL … | 972 | 999 |
| HE S. WANG L. MIAO L. WANG T. DU F. ZHAO L. WANG X. RECEPTOR INTERACTING PROTEIN KINASE-3 DETERMINES CELLULAR NECROTIC RESPONSE TO TNF-ALPHA (2009) | 972 | 989 |
| OBERST A. DILLON C.P. WEINLICH R. MCCORMICK L.L. FITZGERALD P. POP C. HAKEM R. GREEN D.R. CATALYTIC ACTIVITY OF THE CASPASE-8-FLIP(L) | 819 | 834 |
| PELTZER N. RIESER E. TARABORRELLI L. DRABER P. DARDING M. PERNAUTE B. SHIMIZU Y. MONTINARO A. HOIP DEFICIENCY CAUSES EMBRYONIC LETHALITY BY ABERRAN… | 751 | 786 |
| WANG L. DU F. WANG X. TNF-ALPHA INDUCES TWO DISTINCT CASPASE-8 ACTIVATION PATHWAYS (2008) | 721 | 1069 |
| Knowledge Base 5: KB 5: unlabeled (n = 666, density =12.62) | ||
| MIETTINEN M. LASOTA J. GASTROINTESTINAL STROMAL TUMORS: PATHOLOGY AND PROGNOSIS AT DIFFERENT SITES (2006) | 1351 | 1351 |
| HIROTA S. ISOZAKI K. MORIYAMA Y. GAIN-OF-FUNCTION MUTATIONS OF C-KIT IN HUMAN GASTROINTESTINAL STROMAL TUMORS (1998) | 1285 | 1285 |
| VERWEIJ J. CASALI P.G. ZALCBERG J. PROGRESSION-FREE SURVIVAL IN GASTROINTESTINAL STROMAL TUMOURS WITH HIGH-DOSE IMATINIB: RANDOMISED TRIAL (2004) | 1116 | 1116 |
| HEINRICH M.C. CORLESS C.L. DUENSING A. PDGFRA ACTIVATING MUTATIONS IN GASTROINTESTINAL STROMAL TUMORS (2003) | 1070 | 1070 |
| DEMETRI G.D. VON MEHREN M. BLANKE C.D. EFFICACY AND SAFETY OF IMATINIB MESYLATE IN ADVANCED GASTROINTESTINAL STROMAL TUMORS (2002) | 1053 | 1053 |
| DEMETRI G.D. VAN OOSTEROM A.T. GARRETT C.R. EFFICACY AND SAFETY OF SUNITINIB IN PATIENTS WITH ADVANCED GASTROINTESTINAL STROMAL TUMOUR AFTER FAILUR… | 907 | 907 |
| BLANKE C.D. RANKIN C. DEMETRI G.D. PHASE III RANDOMIZED INTERGROUP TRIAL ASSESSING IMATINIB MESYLATE AT TWO DOSE LEVELS IN PATIENTS WITH UNRESECTAB… | 806 | 806 |
| HEINRICH M.C. CORLESS C.L. DEMETRI G.D. KINASE MUTATIONS AND IMATINIB RESPONSE IN PATIENTS WITH METASTATIC GASTROINTESTINAL STROMAL TUMOR (2003) | 739 | 739 |
| CORLESS C.L. BARNETT C.M. HEINRICH M.C. GASTROINTESTINAL STROMAL TUMOURS: ORIGIN AND MOLECULAR ONCOLOGY (2011) | 737 | 737 |
| JOENSUU H. RISK STRATIFICATION OF PATIENTS DIAGNOSED WITH GASTROINTESTINAL STROMAL TUMOR (2008) | 723 | 723 |
| Knowledge Base 6: KB 6: unlabeled (n = 469, density =21.68) | ||
| GERLACH B. CORDIER S.M. SCHMUKLE A.C. EMMERICH C.H. RIESER E. HAAS T.L. LINEAR UBIQUITINATION PREVENTS INFLAMMATION AND REGULATES IMMUNE SIGNALLING… | 1422 | 1744 |
| HAAS T.L. EMMERICH C.H. GERLACH B. SCHMUKLE A.C. CORDIER S.M. RIESER E. RECRUITMENT OF THE LINEAR UBIQUITIN CHAIN ASSEMBLY COMPLEX STABILIZES THE T… | 1111 | 1344 |
| BOISSON B. LAPLANTINE E. DOBBS K. COBAT A. TARANTINO N. HAZEN M. HUMAN HOIP AND LUBAC DEFICIENCY UNDERLIES AUTOINFLAMMATION IMMUNODEFICIENCY AMYLOP… | 931 | 1072 |
| DRABER P. KUPKA S. REICHERT M. DRABEROVA H. LAFONT E. DE MIGUEL D. LUBAC-RECRUITED CYLD AND A20 REGULATE GENE ACTIVATION AND CELL DEATH BY EXERTING… | 890 | 1062 |
| BOISSON B. LAPLANTINE E. PRANDO C. GILIANI S. ISRAELSSON E. XU Z. IMMUNODEFICIENCY AUTOINFLAMMATION AND AMYLOPECTINOSIS IN HUMANS WITH INHERITED HO… | 880 | 998 |
| DAMGAARD R.B. WALKER J.A. MARCO-CASANOVA P. MORGAN N.V. TITHERADGE H.L. ELLIOTT P.R. THE DEUBIQUITINASE OTULIN IS AN ESSENTIAL NEGATIVE REGULATOR O… | 839 | 994 |
| ELLIOTT P.R. LESKE D. HRDINKA M. BAGOLA K. FIIL B.K. MCLAUGHLIN S.H. SPATA2 LINKS CYLD TO LUBAC ACTIVATES CYLD AND CONTROLS LUBAC SIGNALING (2016) | 766 | 910 |
| KEUSEKOTTEN K. ELLIOTT P.R. GLOCKNER L. FIIL B.K. DAMGAARD R.B. KULATHU Y. OTULIN ANTAGONIZES LUBAC SIGNALING BY SPECIFICALLY HYDROLYZING MET1-LINK… | 691 | 971 |
| FIIL B.K. DAMGAARD R.B. WAGNER S.A. KEUSEKOTTEN K. FRITSCH M. BEKKER-JENSEN S. OTULIN RESTRICTS MET1-LINKED UBIQUITINATION TO CONTROL INNATE IMMUNE… | 686 | 811 |
| KUPKA S. DE MIGUEL D. DRABER P. MARTINO L. SURINOVA S. RITTINGER K. SPATA2-MEDIATED BINDING OF CYLD TO HOIP ENABLES CYLD RECRUITMENT TO SIGNALING C… | 678 | 820 |
| Knowledge Base 7: KB 7: unlabeled (n = 402, density =11.88) | ||
| CRAIG R. BEAVIS R.C. TANDEM: MATCHING PROTEINS WITH TANDEM MASS SPECTRA (2004) | 1262 | 1262 |
| COX J. MANN M. MAXQUANT ENABLES HIGH PEPTIDE IDENTIFICATION RATES INDIVIDUALIZED P.P.B.-RANGE MASS ACCURACIES AND PROTEOME-WIDE PROTEIN QUANTIFICAT… | 699 | 1001 |
| ENG J.K. MCCORMACK A.L. YATES J.R. AN APPROACH TO CORRELATE TANDEM MASS SPECTRAL DATA OF PEPTIDES WITH AMINO ACID SEQUENCES IN A PROTEIN DATABASE (… | 420 | 439 |
| ELIAS J.E. GYGI S.P. TARGET-DECOY SEARCH STRATEGY FOR INCREASED CONFIDENCE IN LARGE-SCALE PROTEIN IDENTIFICATIONS BY MASS SPECTROMETRY (2007) | 347 | 374 |
| BOWDEN P. BEAVIS R. MARSHALL J. TANDEM MASS SPECTROMETRY OF HUMAN TRYPTIC BLOOD PEPTIDES CALCULATED BY A STATISTICAL ALGORITHM AND CAPTURED BY A RE… | 312 | 312 |
| SCHWARTZ J.C. SENKO M.W. SYKA J.E. A TWO-DIMENSIONAL QUADRUPOLE ION TRAP MASS SPECTROMETER (2002) | 304 | 304 |
| BENJAMINI Y. HOCHBERG Y. CONTROLLING FALSE DISCOVERY RATE: A PRACTICAL APPROACH TO MULTIPLE TESTING (1995) | 295 | 295 |
| BOWDEN P. META SEQUENCE ANALYSIS OF HUMAN BLOOD PEPTIDES AND THEIR PARENT PROTEINS (2010) | 294 | 294 |
| BOWDEN P. QUANTITATIVE STATISTICAL ANALYSIS OF STANDARD AND HUMAN BLOOD PROTEINS FROM LIQUID CHROMATOGRAPHY ELECTROSPRAY IONIZATION AND TANDEM MASS… | 294 | 294 |
| FLORENTINUS A.K. IDENTIFICATION AND QUANTIFICATION OF PEPTIDES AND PROTEINS SECRETED FROM PROSTATE EPITHELIAL CELLS BY UNBIASED LIQUID CHROMATOGRAP… | 294 | 294 |
In a co-cittion network, the strength of the relationship between a reference pair \(m\) and \(n\) (\(s_{m,n}^{coc}\)) is expressed by the number of publications \(C\) which are jointly citing reference \(m\) and \(n\).
\[s_{m,n}^{coc} = \sum_i c_{i,m} c_{i,n}\]
The intuition here is that references which are frequently cited together are likely to share commonalities in theory, topic, methodology, or context. It can be interpreted as a measure of similarity as evaluated by other researchers that decide to jointly cite both references. Because the publication process is time-consuming, co-citation is a backward-looking measure, which is appropriate to map the relationship between core literature of a field.
This is arguably the more interesting part. Here, we identify the
literature’s current knowledge frontier by carrying out a bibliographic
coupling analysis of the publications in our corpus. This measure uses
bibliographical information of publications to establish a similarity
relationship between them. Again, method details to be found in the tab
Technical description. As you will see, we identify the
main research area, but also a set of adjacent research areas with some
theoretical/methodological/application overlap.
To identify communities in the field’s knowledge frontier (labeled research areas) we again use the Lovain Algorithm (Blondel et al., 2008). We identify the following communities = research areas.
| label | AU | PY | TI | dgr_int | TC | TC_year |
|---|---|---|---|---|---|---|
| Research Area 1: RA 1: unlabeled (n = 1178, density =0.14) | ||||||
| RA 1: unlabeled | GBD 2016 PARKINSON’S D… | 2018 | GLOBAL, REGIONAL, AND NATIONAL BURDEN OF PARKINSON’S DISEASE, 1990–2016: A SYSTEMATIC ANALYSIS FOR THE GLOBAL BURDEN OF DI… | 3.14 | 855 | 213.75 |
| RA 1: unlabeled | ARMSTRONG MJ;OKUN MS | 2020 | DIAGNOSIS AND TREATMENT OF PARKINSON DISEASE: A REVIEW | 1.95 | 507 | 253.50 |
| RA 1: unlabeled | POSTUMA RB;IRANZO A;HU… | 2019 | RISK AND PREDICTORS OF DEMENTIA AND PARKINSONISM IN IDIOPATHIC REM SLEEP BEHAVIOUR DISORDER: A MULTICENTRE STUDY | 2.63 | 313 | 104.33 |
| RA 1: unlabeled | OBESO JA;STAMELOU M;GO… | 2017 | PAST, PRESENT, AND FUTURE OF PARKINSON’S DISEASE: A SPECIAL ESSAY ON THE 200TH ANNIVERSARY OF THE SHAKING PALSY | 2.26 | 363 | 72.60 |
| RA 1: unlabeled | DUNCAN GW;FIRBANK MJ;Y… | 2016 | GRAY AND WHITE MATTER IMAGING: A BIOMARKER FOR COGNITIVE IMPAIRMENT IN EARLY PARKINSON’S DISEASE? | 6.16 | 98 | 16.33 |
| RA 1: unlabeled | TYSNES O-B;STORSTEIN A | 2017 | EPIDEMIOLOGY OF PARKINSON’S DISEASE | 0.62 | 904 | 180.80 |
| RA 1: unlabeled | REICH SG;SAVITT JM | 2019 | PARKINSON’S DISEASE | 3.83 | 137 | 45.67 |
| RA 1: unlabeled | LAWSON RA;YARNALL AJ;D… | 2016 | COGNITIVE DECLINE AND QUALITY OF LIFE IN INCIDENT PARKINSON’S DISEASE: THE ROLE OF ATTENTION | 4.98 | 99 | 16.50 |
| RA 1: unlabeled | BAE YJ;KIM J-M;KIM E;L… | 2016 | LOSS OF NIGRAL HYPERINTENSITY ON 3 TESLA MRI OF PARKINSONISM: COMPARISON WITH 123I-FP-CIT SPECT | 6.64 | 73 | 12.17 |
| RA 1: unlabeled | IRWIN DJ;GROSSMAN M;WE… | 2017 | NEUROPATHOLOGICAL AND GENETIC CORRELATES OF SURVIVAL AND DEMENTIA ONSET IN SYNUCLEINOPATHIES: A RETROSPECTIVE ANALYSIS | 1.93 | 247 | 49.40 |
| Research Area 2: RA 2: unlabeled (n = 1054, density =0.23) | ||||||
| RA 2: unlabeled | SWATEK KN;KOMANDER D | 2016 | UBIQUITIN MODIFICATIONS | 5.51 | 805 | 134.17 |
| RA 2: unlabeled | YAU R;RAPE M | 2016 | THE INCREASING COMPLEXITY OF THE UBIQUITIN CODE | 6.64 | 510 | 85.00 |
| RA 2: unlabeled | MEVISSEN TET;KOMANDER D | 2017 | MECHANISMS OF DEUBIQUITINASE SPECIFICITY AND REGULATION | 5.75 | 432 | 86.40 |
| RA 2: unlabeled | AKUTSU M;DIKIC I;BREMM A | 2016 | UBIQUITIN CHAIN DIVERSITY AT A GLANCE | 8.68 | 257 | 42.83 |
| RA 2: unlabeled | KWON YT;CIECHANOVER A | 2017 | THE UBIQUITIN CODE IN THE UBIQUITIN-PROTEASOME SYSTEM AND AUTOPHAGY | 6.68 | 312 | 62.40 |
| RA 2: unlabeled | ZHENG N;SHABEK N | 2017 | UBIQUITIN LIGASES: STRUCTURE, FUNCTION, AND REGULATION | 3.25 | 515 | 103.00 |
| RA 2: unlabeled | BUETOW L;HUANG DT | 2016 | STRUCTURAL INSIGHTS INTO THE CATALYSIS AND REGULATION OF E3 UBIQUITIN LIGASES | 6.44 | 252 | 42.00 |
| RA 2: unlabeled | OHTAKE F;SAEKI Y;ISHID… | 2016 | THE K48-K63 BRANCHED UBIQUITIN CHAIN REGULATES NF-ΚB SIGNALING | 9.25 | 155 | 25.83 |
| RA 2: unlabeled | CLAGUE MJ;URBÉ S;KOMAN… | 2019 | BREAKING THE CHAINS: DEUBIQUITYLATING ENZYME SPECIFICITY BEGETS FUNCTION | 5.44 | 253 | 84.33 |
| RA 2: unlabeled | OHTAKE F;TSUCHIYA H;SA… | 2018 | K63 UBIQUITYLATION TRIGGERS PROTEASOMAL DEGRADATION BY SEEDING BRANCHED UBIQUITIN CHAINS | 10.00 | 117 | 29.25 |
| Research Area 3: RA 3: unlabeled (n = 671, density =0.23) | ||||||
| RA 3: unlabeled | SPITZER MH;NOLAN GP | 2016 | MASS CYTOMETRY: SINGLE CELLS, MANY FEATURES | 8.42 | 607 | 101.17 |
| RA 3: unlabeled | BECHT E;MCINNES L;HEAL… | 2019 | DIMENSIONALITY REDUCTION FOR VISUALIZING SINGLE-CELL DATA USING UMAP | 2.94 | 1214 | 404.67 |
| RA 3: unlabeled | WOLF FA;ANGERER P;THEI… | 2018 | SCANPY: LARGE-SCALE SINGLE-CELL GENE EXPRESSION DATA ANALYSIS | 2.23 | 1050 | 262.50 |
| RA 3: unlabeled | CHEVRIER S;LEVINE JH;Z… | 2017 | AN IMMUNE ATLAS OF CLEAR CELL RENAL CELL CARCINOMA | 3.18 | 485 | 97.00 |
| RA 3: unlabeled | WEI SC;LEVINE JH;COGDI… | 2017 | DISTINCT CELLULAR MECHANISMS UNDERLIE ANTI-CTLA-4 AND ANTI-PD-1 CHECKPOINT BLOCKADE | 2.26 | 643 | 128.60 |
| RA 3: unlabeled | AZIZI E;CARR AJ;PLITAS… | 2018 | SINGLE-CELL MAP OF DIVERSE IMMUNE PHENOTYPES IN THE BREAST TUMOR MICROENVIRONMENT | 1.69 | 653 | 163.25 |
| RA 3: unlabeled | SAEYS Y;VAN GASSEN S;L… | 2016 | COMPUTATIONAL FLOW CYTOMETRY: HELPING TO MAKE SENSE OF HIGH-DIMENSIONAL IMMUNOLOGY DATA | 3.92 | 252 | 42.00 |
| RA 3: unlabeled | WAGNER J;RAPSOMANIKI M… | 2019 | A SINGLE-CELL ATLAS OF THE TUMOR AND IMMUNE ECOSYSTEM OF HUMAN BREAST CANCER | 3.44 | 272 | 90.67 |
| RA 3: unlabeled | SETTY M;TADMOR MD;REIC… | 2016 | WISHBONE IDENTIFIES BIFURCATING DEVELOPMENTAL TRAJECTORIES FROM SINGLE-CELL DATA | 2.91 | 317 | 52.83 |
| RA 3: unlabeled | HABER AL;BITON M;ROGEL… | 2017 | A SINGLE-CELL SURVEY OF THE SMALL INTESTINAL EPITHELIUM | 1.39 | 603 | 120.60 |
| Research Area 4: RA 4: unlabeled (n = 491, density =0.27) | ||||||
| RA 4: unlabeled | SIDDIQUI I;SCHAEUBLE K… | 2019 | INTRATUMORAL TCF1 + PD-1 + CD8 + T CELLS WITH STEM-LIKE PROPERTIES PROMOTE TUMOR CONTROL IN RESPONSE TO VACCINATION AND CH… | 1.93 | 435 | 145.00 |
| RA 4: unlabeled | CASSETTA L;FRAGKOGIANN… | 2019 | HUMAN TUMOR-ASSOCIATED MACROPHAGE AND MONOCYTE TRANSCRIPTIONAL LANDSCAPES REVEAL CANCER-SPECIFIC REPROGRAMMING, BIOMARKERS… | 1.39 | 313 | 104.33 |
| RA 4: unlabeled | JIA D;LI S;LI D;XUE H;… | 2018 | MINING TCGA DATABASE FOR GENES OF PROGNOSTIC VALUE IN GLIOBLASTOMA MICROENVIRONMENT | 2.06 | 186 | 46.50 |
| RA 4: unlabeled | HUNDHAUSEN C;ROTH A;WH… | 2016 | ENHANCED T CELL RESPONSES TO IL-6 IN TYPE 1 DIABETES ARE ASSOCIATED WITH EARLY CLINICAL DISEASE AND INCREASED IL-6 RECEPTO… | 3.56 | 57 | 9.50 |
| RA 4: unlabeled | CHEN L;LU D;SUN K;XU Y… | 2019 | IDENTIFICATION OF BIOMARKERS ASSOCIATED WITH DIAGNOSIS AND PROGNOSIS OF COLORECTAL CANCER PATIENTS BASED ON INTEGRATED BIO… | 3.41 | 58 | 19.33 |
| RA 4: unlabeled | CHAUDHARY K;POIRION OB… | 2018 | DEEP LEARNING–BASED MULTI-OMICS INTEGRATION ROBUSTLY PREDICTS SURVIVAL IN LIVER CANCER | 0.56 | 347 | 86.75 |
| RA 4: unlabeled | MAN K;GABRIEL SS;LIAO … | 2017 | TRANSCRIPTION FACTOR IRF4 PROMOTES CD8+ T CELL EXHAUSTION AND LIMITS THE DEVELOPMENT OF MEMORY-LIKE T CELLS DURING CHRONIC… | 1.17 | 166 | 33.20 |
| RA 4: unlabeled | OMENETTI S;BUSSI C;MET… | 2019 | THE INTESTINE HARBORS FUNCTIONALLY DISTINCT HOMEOSTATIC TISSUE-RESIDENT AND INFLAMMATORY TH17 CELLS | 1.32 | 106 | 35.33 |
| RA 4: unlabeled | CHEN L;YUAN L;WANG Y;W… | 2017 | CO-EXPRESSION NETWORK ANALYSIS IDENTIFIED FCER1G IN ASSOCIATION WITH PROGRESSION AND PROGNOSIS IN HUMAN CLEAR CELL RENAL C… | 1.93 | 71 | 14.20 |
| RA 4: unlabeled | MEYER-SCHALLER N;CARDN… | 2019 | A HIERARCHICAL REGULATORY LANDSCAPE DURING THE MULTIPLE STAGES OF EMT | 3.34 | 36 | 12.00 |
| Research Area 5: RA 5: unlabeled (n = 369, density =0.65) | ||||||
| RA 5: unlabeled | MEHREN MV;JOENSUU H | 2018 | GASTROINTESTINAL STROMAL TUMORS | 9.83 | 129 | 32.25 |
| RA 5: unlabeled | JOENSUU H;ERIKSSON M;S… | 2016 | ADJUVANT IMATINIB FOR HIGH-RISK GI STROMAL TUMOR: ANALYSIS OF A RANDOMIZED TRIAL | 8.80 | 135 | 22.50 |
| RA 5: unlabeled | JOENSUU H;WARDELMANN E… | 2017 | EFFECT OF KIT AND PDGFRA MUTATIONS ON SURVIVAL IN PATIENTS WITH GASTROINTESTINAL STROMAL TUMORS TREATED WITH ADJUVANT IMAT… | 6.98 | 93 | 18.60 |
| RA 5: unlabeled | BLAY J-Y;SERRANO C;HEI… | 2020 | RIPRETINIB IN PATIENTS WITH ADVANCED GASTROINTESTINAL STROMAL TUMOURS (INVICTUS): A DOUBLE-BLIND, RANDOMISED, PLACEBO-CONT… | 5.20 | 103 | 51.50 |
| RA 5: unlabeled | NISHIDA T;BLAY J-Y;HIR… | 2016 | THE STANDARD DIAGNOSIS, TREATMENT, AND FOLLOW-UP OF GASTROINTESTINAL STROMAL TUMORS BASED ON GUIDELINES | 2.56 | 209 | 34.83 |
| RA 5: unlabeled | CASALI PG;FUMAGALLI E;… | 2017 | TEN-YEAR PROGRESSION-FREE AND OVERALL SURVIVAL IN PATIENTS WITH UNRESECTABLE OR METASTATIC GI STROMAL TUMORS: LONG-TERM AN… | 5.35 | 92 | 18.40 |
| RA 5: unlabeled | RAUT CP;ESPAT NJ;MAKI … | 2018 | EFFICACY AND TOLERABILITY OF 5-YEAR ADJUVANT IMATINIB TREATMENT FOR PATIENTS WITH RESECTED INTERMEDIATE- OR HIGH-RISK PRIM… | 9.00 | 50 | 12.50 |
| RA 5: unlabeled | HEINRICH MC;JONES RL;V… | 2020 | AVAPRITINIB IN ADVANCED PDGFRA D842V-MUTANT GASTROINTESTINAL STROMAL TUMOUR (NAVIGATOR): A MULTICENTRE, OPEN-LABEL, PHASE … | 4.59 | 90 | 45.00 |
| RA 5: unlabeled | KOO D-H;RYU M-H;KIM K-… | 2016 | ASIAN CONSENSUS GUIDELINES FOR THE DIAGNOSIS AND MANAGEMENT OF GASTROINTESTINAL STROMAL TUMOR | 4.54 | 90 | 15.00 |
| RA 5: unlabeled | AKAHOSHI K;OYA M;KOGA … | 2018 | CURRENT CLINICAL MANAGEMENT OF GASTROINTESTINAL STROMAL TUMOR | 4.01 | 98 | 24.50 |
| Research Area 6: RA 6: unlabeled (n = 330, density =0.38) | ||||||
| RA 6: unlabeled | PINO LK;SEARLE BC;BOLL… | 2020 | THE SKYLINE ECOSYSTEM: INFORMATICS FOR QUANTITATIVE MASS SPECTROMETRY PROTEOMICS | 2.61 | 193 | 96.50 |
| RA 6: unlabeled | THE M;MACCOSS MJ;NOBLE… | 2016 | FAST AND ACCURATE PROTEIN FALSE DISCOVERY RATES ON LARGE-SCALE PROTEOMICS DATA SETS WITH PERCOLATOR 3.0 | 3.61 | 130 | 21.67 |
| RA 6: unlabeled | LANGELLA O;VALOT B;BAL… | 2017 | X!TANDEMPIPELINE: A TOOL TO MANAGE SEQUENCE REDUNDANCY FOR PROTEIN INFERENCE AND PHOSPHOSITE IDENTIFICATION | 4.36 | 100 | 20.00 |
| RA 6: unlabeled | GEYER PE;HOLDT LM;TEUP… | 2017 | REVISITING BIOMARKER DISCOVERY BY PLASMA PROTEOMICS | 1.34 | 318 | 63.60 |
| RA 6: unlabeled | GESSULAT S;SCHMIDT T;Z… | 2019 | PROSIT: PROTEOME-WIDE PREDICTION OF PEPTIDE TANDEM MASS SPECTRA BY DEEP LEARNING | 1.11 | 224 | 74.67 |
| RA 6: unlabeled | GEYER PE;WEWER ALBRECH… | 2016 | PROTEOMICS REVEALS THE EFFECTS OF SUSTAINED WEIGHT LOSS ON THE HUMAN PLASMA PROTEOME | 1.59 | 118 | 19.67 |
| RA 6: unlabeled | RANDLES MJ;HUMPHRIES M… | 2017 | PROTEOMIC DEFINITIONS OF BASEMENT MEMBRANE COMPOSITION IN HEALTH AND DISEASE | 2.00 | 76 | 15.20 |
| RA 6: unlabeled | WICHMANN C;MEIER F;WIN… | 2019 | MAXQUANT.LIVE ENABLES GLOBAL TARGETING OF MORE THAN 25,000 PEPTIDES | 3.09 | 47 | 15.67 |
| RA 6: unlabeled | TSOU C-C;TSAI C-F;TEO … | 2016 | UNTARGETED, SPECTRAL LIBRARY-FREE ANALYSIS OF DATA-INDEPENDENT ACQUISITION PROTEOMICS DATA GENERATED USING ORBITRAP MASS S… | 2.90 | 46 | 7.67 |
| RA 6: unlabeled | SCHWENK JM;OMENN GS;SU… | 2017 | THE HUMAN PLASMA PROTEOME DRAFT OF 2017: BUILDING ON THE HUMAN PLASMA PEPTIDEATLAS FROM MASS SPECTROMETRY AND COMPLEMENTAR… | 1.21 | 109 | 21.80 |
| Research Area 7: RA 7: unlabeled (n = 299, density =3.15) | ||||||
| RA 7: unlabeled | HESTHAVEN JS;UBBIALI S | 2018 | NON-INTRUSIVE REDUCED ORDER MODELING OF NONLINEAR PROBLEMS USING NEURAL NETWORKS | 7.80 | 154 | 38.50 |
| RA 7: unlabeled | LIEVENS A;JACCHIA S;KA… | 2016 | MEASURING DIGITAL PCR QUALITY: PERFORMANCE PARAMETERS AND THEIR OPTIMIZATION | 10.90 | 74 | 12.33 |
| RA 7: unlabeled | BANGALORE P;LETZGUS S;… | 2017 | AN ARTIFICIAL NEURAL NETWORK-BASED CONDITION MONITORING METHOD FOR WIND TURBINES, WITH APPLICATION TO THE MONITORING OF TH… | 9.54 | 79 | 15.80 |
| RA 7: unlabeled | ASKHAM T;KUTZ JN | 2018 | VARIABLE PROJECTION METHODS FOR AN OPTIMIZED DYNAMIC MODE DECOMPOSITION | 8.06 | 73 | 18.25 |
| RA 7: unlabeled | LAZZARI F;BUFFI A;NEPA… | 2017 | NUMERICAL INVESTIGATION OF AN UWB LOCALIZATION TECHNIQUE FOR UNMANNED AERIAL VEHICLES IN OUTDOOR SCENARIOS | 11.58 | 41 | 8.20 |
| RA 7: unlabeled | LASSENBERGER A;GRÜNEWA… | 2017 | MONODISPERSE IRON OXIDE NANOPARTICLES BY THERMAL DECOMPOSITION: ELUCIDATING PARTICLE FORMATION BY SECOND-RESOLVED IN SITU … | 7.75 | 60 | 12.00 |
| RA 7: unlabeled | BARBIERI S;DONATI OF;F… | 2016 | IMPACT OF THE CALCULATION ALGORITHM ON BIEXPONENTIAL FITTING OF DIFFUSION-WEIGHTED MRI IN UPPER ABDOMINAL ORGANS | 7.82 | 58 | 9.67 |
| RA 7: unlabeled | ROBERT DJ;RAJEEV P;KOD… | 2016 | EQUATION TO PREDICT MAXIMUM PIPE STRESS INCORPORATING INTERNAL AND EXTERNAL LOADINGS ON BURIED PIPES | 13.27 | 28 | 4.67 |
| RA 7: unlabeled | AMIGO JM;DEL OLMO ALVA… | 2016 | STALING OF WHITE WHEAT BREAD CRUMB AND EFFECT OF MALTOGENIC Α-AMYLASES. PART 1: SPATIAL DISTRIBUTION AND KINETIC MODELING … | 9.76 | 38 | 6.33 |
| RA 7: unlabeled | ARSLAN D;CHONG KE;MIRO… | 2017 | ANGLE-SELECTIVE ALL-DIELECTRIC HUYGENS’ METASURFACES | 9.43 | 39 | 7.80 |
In a bibliographic coupling network, the coupling-strength between publications is determined by the number of commonly cited references they share, assuming a common pool of references to indicate similarity in context, methods, or theory. Formally, the strength of the relationship between a publication pair \(i\) and \(j\) (\(s_{i,j}^{bib}\)) is expressed by the number of commonly cited references.
\[s_{i,j}^{bib} = \sum_m c_{i,m} c_{j,m}\]
Since our corpus contains publications which differ strongly in terms of the number of cited references, we normalize the coupling strength by the Jaccard similarity coefficient. Here, we weight the intercept of two publications’ bibliography (shared refeences) by their union (number of all references cited by either \(i\) or \(j\)). It is bounded between zero and one, where one indicates the two publications to have an identical bibliography, and zero that they do not share any cited reference. Thereby, we prevent publications from having high coupling strength due to a large bibliography (e.g., literature surveys).
\[S_{i,j}^{jac-bib} =\frac{C(i \cap j)}{C(i \cup j)} = \frac{s_{i,j}^{bib}}{c_i + c_j - s_{i,j}^{bib}}\]
More recent articles have a higher pool of possible references to co-cite to, hence they are more likely to be coupled. Consequently, bibliographic coupling represents a forward looking measure, and the method of choice to identify the current knowledge frontier at the point of analysis.
All results are preliminary so far…